Nicolas Jorquera

About Nicolas Jorquera

Hey! I’m Nicolas Jorquera, an enthusiastic and motivated data scientist with a deep interest in technological innovation. After completing a Bachelor in Mechanical Engineering I chose to transition from Robotics to Data Science.I chose engineering because I always enjoyed learning how things worked. Mechanical Engineering seemed obvious at first because mechanical systems were everywhere, and they were easy to visualize and disassemble in my head. However as I worked on various systems throughout the years [see projects below], I began to gravitate away from hands on systems. I dabbled with Electrical Systems and decided to pursue a minor in Robotics; where I got first-hand experience into the workings of advanced coding.[ Not just printing “hello world” =)]. After that I couldn’t get enough. After graduating; I joined Springboard Data Science Track, a six-month intensive course in data science, machine learning, Python, and SQL, where I specialized in the Advanced Machine Learning Track; and got to work along-side data scientists and understand the field better. This journey led me to further my understanding and skills in the field of Data Science. To achieve this, I embarked on a Master\'s program in Data Science, which provided me with a comprehensive understanding of the subject. This program was not just an academic pursuit but a strategic step to deepen my expertise in the latest technologies and methodologies in Data Science. It was an opportunity to blend my engineering background with advanced data science techniques, enhancing my ability to contribute innovatively in this rapidly evolving field.SKILLS: Data Science, Deep Learning, Python, SQL, Pandas, NumPy, SciPy, Scikit-learn, Keras, XGBoost, LightGBM, NLTK, AWS SageMaker, Apache Spark, PySpark, Flask, Git, Data Mining, Data Wrangling, Visualization, Data Storytelling, Logistic Regression, Linear Regression, Regularization, LASSO, Ridge, Ensemble Methods, Gradient Boosting, Random Forests, Neural Networks, K-Nearest Neighbors, Support Vector Machines, Gradient Descent, PCA, K-means Clustering, Natural Language Processing, ANOVA, Hypothesis Testing, A/B Testing, Algorithms, Data Structures, Microsoft Office, HTML, CSS.

Experience

1. Ai Engineer

   IBM

   Jun 2025 — Present · NY, US

   Delivered AI strategy, engineering, and GenAI solutions across Fortune 500 insurance, financial services, and wealth management clients. Designed and implemented a GenAI proof-of-concept on IBM\'s AI platform for a leading national insurer, integrating LLM pipelines tailored to insurance risk scenarios. Conducted prompt sensitivity analysis, few-shot tuning, and iterative refinement to optimize accuracy and contextual relevance. Applied enterprise LLM evaluation frameworks to assess robustness and explainability, presenting strategic findings to senior client leadership. Led the design of multiple agentic AI workflows for a Mobile Redesign initiative, owning Android-focused pipelines that generated production-ready code and improved developer productivity by ~40%. Implemented custom agent orchestration incorporating RAG, Human-in-the-Loop controls, and context engineering to ensure scalability and alignment with engineering best practices. Co-led development of a Risk Identification solution integrating structured and unstructured data sources to surface emerging risks and generate mitigation recommendations aligned to client compliance frameworks — directly supporting a new ERM engagement launch. Built on LangChain/LangSmith for fine-grained observability and AI governance. Presented in person to the Chief Risk Officer and international leadership, securing executive buy-in. The demo was subsequently showcased at an industry Risk Conference in London and reused in additional client pursuits. Served as Senior Data Scientist on an AI transformation feasibility assessment for a global wealth management firm. Led process mapping workshops, designed proof-of-concept experiments leveraging Microsoft\'s AI ecosystem, and mapped and prioritized AI use cases by feasibility and business impact, delivering actionable recommendations that shaped the client\'s AI adoption roadmap

Education

• The High School for Math, Science and Engineering

  High School Diploma, Mechanical Engineering

• NYU Tandon School of Engineering

  Bachelor of Science - BS, Mechanical Engineering

• Stevens Institute of Technology

  Master of Science - MS, Data Science